Image processing device, image processing method, and image processing program

ABSTRACT

An image processing device according to one embodiment includes an acquisition unit, a generation unit, a calculation unit, and an estimation unit. The acquisition unit acquires an input image. The generation unit generates a plurality of comparison images by superimposing each of a plurality of noises with different densities from each other on a target region being at least part of the input image. The calculation unit calculates, for each of the plurality of comparison images, the degradation level of the comparison image with respect to the input image. The estimation unit estimates the noise level of the input image based on a plurality of calculated degradation levels.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a National Stage of International Application No.PCT/JP2017/020845 filed Jun. 5, 2017.

TECHNICAL FIELD

One aspect of the present invention relates to an image processingdevice, an image processing method, and an image processing program.

BACKGROUND ART

To process an image containing noise, it is necessary to estimate thenoise level in advance. The technique disclosed in Non Patent Literature1 makes actual measurements of a camera to be used for taking images andthereby constructs a noise model in consideration of the cameracharacteristics.

CITATION LIST Non Patent Literature

NPL1: Kenji Kamimura, Hitomi Ito, Norimichi Tsumura, Toshiya Nakaguchi,and Yoichi Miyake, “Evaluation and Improvement of Noise Estimation froma Single Image”, The journal of the Institute of Image Information andTelevision Engineers, Vol. 63, No. 1, pp. 101-104, 2009

SUMMARY OF INVENTION Technical Problem

In the technique disclosed in Non Patent Literature 1, it is necessaryto model the relationship between the camera characteristics and thenoise level in advance. Therefore, it is not possible to estimate thenoise level unless obtaining prior information such as a noise model orcamera information. It is thus desirable to estimate the noise level ofan input image without prior information.

Solution to Problem

An image processing device according to one aspect of the presentinvention includes an acquisition unit configured to acquire an inputimage, a generation unit configured to generate a plurality ofcomparison images by superimposing each of a plurality of noises withdifferent densities from each other on a target region being at leastpart of the input image, a calculation unit configured to calculate, foreach of the plurality of comparison images, a degradation level of thecomparison image with respect to the input image, and an estimation unitconfigured to estimate a noise level of the input image based on aplurality of calculated degradation levels.

An image processing method according to one aspect of the presentinvention is an image processing method performed by an image processingdevice including a processor, the method including an acquisition stepof acquiring an input image, a generation step of generating a pluralityof comparison images by superimposing each of a plurality of noises withdifferent densities from each other on a target region being at leastpart of the input image, a calculation step of calculating, for each ofthe plurality of comparison images, a degradation level of thecomparison image with respect to the input image, and an estimation stepof estimating a noise level of the input image based on a plurality ofcalculated degradation levels.

An image processing program according to one aspect of the presentinvention causes a computer to execute an acquisition step of acquiringan input image, a generation step of generating a plurality ofcomparison images by superimposing each of a plurality of noises withdifferent densities from each other on a target region being at leastpart of the input image, a calculation step of calculating, for each ofthe plurality of comparison images, a degradation level of thecomparison image with respect to the input image, and an estimation stepof estimating a noise level of the input image based on a plurality ofcalculated degradation levels.

In the above-described aspects, a plurality of comparison images areobtained by superimposing a plurality of noises on an input image. Theinput image is not substantially degraded when noise with lower densitythan inherent noise of the input image is superimposed on the inputimage. On the other hand, the input image is degraded when noise withhigher density than inherent noise of the input image is superimposed onthe input image. With use of such characteristics related to noise, bygenerating a plurality of comparison images from an input image andestimating the noise level of the input image using those comparisonimages, it is possible to estimate the noise level of the input imagewithout prior information.

Advantageous Effects of Invention

According to one aspect of the present invention, it is possible toestimate the noise level of an input image without prior information.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a view showing the concept of estimation of a noise level inan embodiment.

FIG. 2 is a view showing the hardware configuration of an imageprocessing device according to the embodiment.

FIG. 3 is a view showing the functional configuration of the imageprocessing device according to the embodiment.

FIG. 4 is a flowchart showing a process in the image processing deviceaccording to the embodiment.

FIG. 5 is view showing an example of processing of generating acomparative image.

FIG. 6 is a view illustrating an example of estimation of a noise level.

FIG. 7 is a view illustrating an example of estimation of a noise level.

FIG. 8 is a view showing the configuration of an image processingprogram according to the embodiment.

DESCRIPTION OF EMBODIMENTS

An embodiment of the present invention is described hereinafter withreference to the attached drawings. Note that, in the description of thedrawings, the same elements are denoted by the same reference symbolsand redundant description thereof is omitted.

Overview

An image processing device 10 according to an embodiment is a computeror a computer system that objectively estimates the noise level of animage.

“Image” is an image where an object is fixed on a certain medium so thatit is perceivable by human eyes. The image becomes visually perceivableby processing data indicating an image (image data), which can beprocessed by a computer. To be specific, the image becomes visuallyperceivable by being recorded in a storage device such as a memory andoutput to an output device such as a monitor by processing of aprocessor. The image may be a still image or each frame that forms amoving image.

“Noise” is unnecessary data or data irregularities contained in animage, which is one of factors that degrade the image quality. “Noiselevel” is an index indicating how much noise an image contains. In thisembodiment, the noise level is represented by the standard deviation σof the Gaussian noise, which is one type of random noise, as an example.The Gaussian noise can be represented as N(a,σ), using the average a andthe standard deviation σ.

FIG. 1 is a view showing the concept of estimation of the noise level inthis embodiment. In this example, it is assumed that an image processingdevice 10 estimates the noise level of an input image 21 whose actualnoise level is 10 (which corresponds to the Gaussian noise N(0,10)).First, the image processing device 10 generates a plurality ofcomparison images by superimposing a plurality of noises with differentdensities from each other on the input image 21. The “density” of noiseis an index indicating the amount or intensity of noise. As the densityis higher, noise is more intense and appears more clearly on an image.In this embodiment, the image processing device 10 superimposes randomnoise (noise that appears randomly all over an image) on the input image21. To be more specific, the image processing device 10 superimposes theGaussian noise, which is one type of random noise, on the input image21. In this embodiment, the density of random noise is also referred tosimply as “noise density”. In the Gaussian noise, the noise density isrepresented by the standard deviation σ, and therefore the noise densityis higher as the standard deviation σ is higher.

In the example of FIG. 1 , the image processing device 10 superimposesthe Gaussian noise N(0,2) on the input image 21 and thereby generates acomparison image 22. Further, the image processing device 10superimposes the Gaussian noise N(0,10) on the input image 21 andthereby generates a comparison image 23, and superimposes the Gaussiannoise N(0,18) on the input image 21 and thereby generates a comparisonimage 24. In this manner, the image processing device 10 generatescomparison images while changing the noise density (for example, whilechanging the standard deviation to 26, 34, etc.). Note that, in FIG. 1 ,each Gaussian noise is schematically represented by a collection ofdots.

When noise is contained in an image that should be clear, the imagequality is initially low. The image quality is the quality of appearanceof an image, and it is determined by various factors such as resolution,tone and contrast. The presence or absence of noise is one of suchfactors, and the image quality is degraded when the image containsnoise. When the noise density to be superimposed is equal to or lessthan the inherent noise density of the original image, the image qualityis not substantially degraded even if the random noise is superimposed.Thus, in this case, the image quality of the comparison image is thesame or substantially the same as that of the original image. On theother hand, when the noise density to be superimposed is more than theinherent noise density of the original image, the image quality of thecomparison image becomes lower than that of the original image.

In the example of FIG. 1 , it is assumed that the noise level of theinput image 21 is 10, which corresponds to the Gaussian noise N(0,10).Assume the case where the input image 21 is obtained by superimposingthe Gaussian noise N(0,10) on an image not containing any noise. In thiscase, the noise level of the comparison image 22 obtained bysuperposition of the Gaussian noise N(0,2) is also 10. Further, thenoise level of the comparison image 23 obtained by superposition of theGaussian noise N(0,10) is also 10. However, because the Gaussian noiseN(0,18) has higher density than the inherent Gaussian noise N(0,10) ofthe input image 21, the noise level of the comparison image 24 obtainedby superposition of the Gaussian noise N(0,18) is higher than 10 (forexample, the noise level becomes 18). The noise level of the comparisonimage is also higher than 10 when the standard deviation of the Gaussiannoise is further increased. Therefore, the noise level of the inputimage 21 is assumed to be 10, or a value between 10 to 18.

In this manner, the image processing device 10 generates a plurality ofcomparison images by superimposing a plurality of noises on an originalimage and determines the degree of deterioration of the image quality ofthose comparison images, and thereby estimates the noise level of theoriginal image.

Configuration of Device

FIG. 2 shows a typical hardware configuration of the image processingdevice 10. The image processing device 10 includes a processor 101 thatruns an operating system, an application program and the like, a mainstorage unit 102 such as ROM and RAM, an auxiliary storage unit 103 suchas a hard disk or a flash memory, a communication control unit 104 suchas a network card or a wireless communication module, an input device105 such as a keyboard and a mouse, and an output device 106 such as amonitor.

The functional elements of the image processing device 10 areimplemented by loading given software (for example, an image processingprogram P1, which is described later) onto the processor 101 or the mainstorage device 102 and running the program. The processor 101 makes thecommunication control device 104, the input device 105 or the outputdevice 106 operate in accordance with the software, and reads and writesdata to and from the main storage device 102 or the auxiliary storagedevice 103. Data or databases required for the processing are stored inthe main storage device 102 or the auxiliary storage device 103.

The image processing device 10 may be composed of a single computer or aplurality of computers. In the case of using a plurality of computers,those computers are connected through a communication network such asthe Internet or an intranet, and thereby one image processing device 10is logically constructed.

FIG. 3 shows the functional configuration of the image processing device10. In this embodiment, the image processing device 10 includes anacquisition unit 11, a generation unit 12, a calculation unit 13, and anestimation unit 14 as functional elements.

The acquisition unit 11 is a functional element that acquires an inputimage. The input image is an image to be processed to estimate the noiselevel thereof. The input image may be referred to as an original image.

The generation unit 12 is a functional element that generates aplurality of comparison images to be used to estimate the noise level ofthe acquired input image. The generation unit 12 obtains a comparisonimage by setting at least part of the input image as a target region andsuperimposing random noise on the target region. The comparison image isan image obtained by intentionally degrading the image quality of theinput image. Note that, however, there is a case where the noise levelcontained in the comparison image is the same or substantially the sameas that in the input image as described above with reference to FIG. 1 .The “target region” is a range composed of pixels arranged in a row. Thetarget region may be only part of the input image or the whole of theinput image.

The calculation unit 13 is a functional element that calculates, foreach of the plurality of comparison images obtained by the generationunit 12, the degradation level of the comparison image with respect tothe input image. Thus, the calculation unit 13 obtains a plurality ofdegradation levels for one input image. The “degradation level” is anindex indicating how much the image quality of the comparison image isdegraded compared with the image quality of the input image due to theeffect of random noise. The calculation unit 13 outputs the plurality ofcalculated degradation levels to the estimation unit 14.

The estimation unit 14 is a functional element that estimates the noiselevel of the input image based on a plurality of calculated degradationlevels.

Operation of Device

The operation of the image processing device 10 and an image processingmethod according to this embodiment are described hereinafter withreference to FIGS. 4 to 7 .

FIG. 4 is a flowchart showing a process in the image processing device10. First, the acquisition unit 11 acquires one input image (Step S11,acquisition step). A method of acquiring the input image is notparticularly limited. For example, the acquisition unit 11 may acquirethe input image by accessing an image database that stores arbitraryimages. Note that the image database may be a separate device from theimage processing device 10 or may be part of the image processing device10. Alternatively, the acquisition unit 11 may acquire the input imageinput or designated by a user of the image processing device 10.Alternatively, the acquisition unit 11 may receive the input image fromanother computer.

The acquisition unit 11 then converts the input image to a grayscaleimage (Step S12). The grayscale image is an image whose pixel value doesnot contain information other than the luminous intensity. Because theGaussian noise superimposed on the input image does not have colorinformation, it is possible to obtain the noise level more accurately byconverting the input image to the grayscale image in accordance with theGaussian noise.

After that, the generation unit 12 generates a plurality of comparisonimages from the input image (Step S13, generation step). The generationunit 12 generates one comparison image as follows. The generation unit12 reproduces the input image converted into grayscale and therebyobtains an input image for generating a comparison image (which is alsoreferred to hereinafter simply as “input image”). Then, the generationunit 12 sets at least part of this input image as a target region,reduces the image quality of the target region and thereby obtains acomparison image. The generation unit 12 obtains the comparison image bysuperimposing the Gaussian noise on the target region.

FIG. 5 is a view showing an example of processing of generating acomparison image. In this example, the generation unit 12 superimposesGaussian noise 33 indicated by a normal distribution 32 on a targetregion 31 with 4 (pixels)×4 (pixels), and thereby generates a targetregion 34 that forms at least part of a comparison image. Because theluminous intensity of at least part of the target region 31 changes bythis processing, noise occurs in the target region 34. In FIG. 5 ,pixels where the luminous intensity has changed by superposition ofnoise are indicated by hatching.

The generation unit 12 generates a plurality of comparison images whilechanging the standard deviation of the Gaussian noise. This meansgenerating a plurality of comparison images with different densities(intensities) of random noise from each other. Setting of the standarddeviation of the Gaussian noise and the number of comparison images arenot particularly limited. For example, the generation unit 12 maygenerate a plurality of comparison images while changing the standarddeviation to 2, 10, 18, 26, etc., or may generate a plurality ofcomparison images while changing the standard deviation to 2, 6, 10, 14,etc.

Then, the calculation unit 13 calculates the degradation level of eachcomparison image with respect to the input image (Step S14, calculationstep). In this embodiment, the calculation unit 13 uses structuralsimilarity (SSIM) as the degradation level. The SSIM is a technique andan index that estimate the similarity between two images by a product ofa difference in average luminance, a difference in standard deviation ofpixel value, and a covariance between pixels. In general, the SSIM isconsidered to be an index close to the human subjective decision. TheSSIM between two images is a value between 0 and 1, and as its value ishigher, the two images are more similar to each other.

When the SSIM in two images x and y is represented as SSIM(x,y), theSSIM(x,y) is obtained by the following Equation 1:

$\begin{matrix}{{{SSIM}\left( {x,y} \right)} = \frac{\left( {{2\mu_{x}\mu_{y}} + c_{1}} \right)\left( {{2\sigma_{xy}} + c_{2}} \right)}{\left( {\mu_{x}^{2} + \mu_{y}^{2} + c_{1}} \right)\left( {\sigma_{x}^{2} + \sigma_{y}^{2} + c_{2}} \right)}} & (1)\end{matrix}$

The meaning of each variable in this equation is as follows.

-   -   μ_(x): average pixel value of image x    -   μ_(y): average pixel value of image y    -   σ_(x): standard deviation of pixel value of image x    -   σ_(y): standard deviation of pixel value of image y    -   σ_(xy): covariance of images x, y    -   c₁=(k₁L)²,c₂=(k₂L)²: constant to stabilize an evaluation value        even when a denominator value is extremely small. L is a dynamic        range of a pixel value (255 in an 8-bit image). k₁ and k₂ are        values that can be set arbitrarily, and their initial values are        generally set to k₁=0.01 and k₂=0.03.

The calculation unit 13 calculates the SSIM between the input image(image x) and the comparison image (image y). In the example of FIG. 1 ,the calculation unit 13 at least calculates SSIM₁ obtained from theinput image 21 and the comparison image 22, SSIM₂ obtained from theinput image 21 and the comparison image 23, and SSIM₃ obtained from theinput image 21 and the comparison image 24.

Then, the estimation unit 14 estimates the noise level of the inputimage based on a plurality of degradation levels (SSIM in thisembodiment) (Step S15, estimation step). To be more specific, theestimation unit 14 calculates the relationship between the noise densityand the degradation level and estimates the noise level based on thisrelationship. In this embodiment, the noise density (the standarddeviation σ of the Gaussian noise) is used as the noise level. A methodof estimating the noise level is not particularly limited.

For example, the estimation unit 14 may estimate the noise level bycomparing a plurality of SSIM with a threshold. To be specific, theestimation unit 14 may calculate the relationship between the noisedensity (the standard deviation of the Gaussian noise) and the SSIM, andestimate the noise density when the SSIM coincides with the threshold,as the noise level. The estimation unit 14 can identify the noisedensity when the SSIM coincides with the threshold by obtaining afunction representing the relationship between the noise density and theSSIM with use of the least squares method, the linear interpolation orthe like. The threshold may be varied according to the input image.

Alternatively, the estimation unit 14 may approximate the relationshipbetween the noise density (the standard deviation of the Gaussian noise)and the SSIM by a non-linear function, and estimate the noise levelbased on the leading coefficient of this non-linear function (to be morespecific, the leading coefficient of a polynomial that defines thenon-linear function). The non-linear function is not particularlylimited, and it may be a quadratic function, a cubic function, or ahigher-order function, for example. A method of approximation by thenon-linear function is also not limited, and the least-squares methodmay be used, for example. When obtaining the non-linear function, theestimation unit 14 normalizes the SSIM. This normalization is processingto standardize the level of SSIM between images.

If the relationship between the noise density (the standard deviation ofthe Gaussian noise) and the SSIM of each image is represented by a graphwhen the noise level is the same or approximate between a plurality ofimages with different subjects, the degree of curve (the curvature) ofeach graph is almost similar to each other. On the other hand, therelationship between the noise density and the SSIM varies when thenoise level is different even if an image is the same.

FIG. 6 is a graph obtained by applying five types of noise levels (2, 6,10, 14 and 18) to one image and then plotting the relationship betweenthe noise density (the standard deviation of the Gaussian noise) and theSSIM for each of the noise levels. As shown therein, the curvaturevaries when the noise level is different even if the image is the same.When the noise level of the original image is low, the image qualitybegins to be degraded (the SSIM begins to decrease) at the point whererandom noise with low density is superimposed. On the other hand, whenthe noise level of the original image is high, the image quality is notdegraded so much (the SSIM does not decrease so much) even if randomnoise with low density is superimposed. Thus, it is possible to estimatethe noise level from the degree of decrease in the SSIM with an increasein the noise density. Because the curvature of the graph indicating adecrease in the SSIM is significantly dependent on the leadingcoefficient of a non-linear function, it is possible to estimate thenoise level of various images based on a standardized reference by usingthis leading coefficient. When each of the graphs in FIG. 6 isapproximated by the quadratic function “y=ax²+bx+c”, the estimation unit14 estimates the noise level with use of the coefficient a.

FIG. 7 is a graph showing an example of the correspondence between thenoise level and the leading coefficient of the quadratic function (thenon-linear function representing the relationship between the noisedensity and the SSIM) for five types of images A to E. The horizontalaxis of the graph corresponds to the noise level, and the vertical axisof the graph indicates the leading coefficient of the quadraticfunction. Each quadratic function approximates the relationship betweenthe noise density and the SSIM, which is obtained by calculating theSSIM while changing the noise density (the standard deviation of theGaussian noise) to 1, 2, 3, 4, etc. and normalizing each SSIM.

As shown in FIG. 7 , the leading coefficient of the quadratic function(the non-linear function) obtained from the normalized SSIM is within acertain range according to the noise level regardless of the image type.Therefore, the noise level of the image can be determined by setting thecorrespondence between the leading coefficient and the noise level inadvance and then determining to which noise level the obtained leadingcoefficient corresponds. A method of representing the correspondence isnot particularly limited, and it may be represented by a table(correspondence table), for example. The image processing device 10(e.g., the estimation unit 14) previously stores this correspondence. Inthe example of FIG. 7 , the noise level is 2 when the leadingcoefficient is equal to or more than Ta, the noise level is 6 when theleading coefficient is equal to or more than Tb and less than Ta, thenoise level is 10 when the leading coefficient is equal to or more thanTc and less than Tb, and the noise level is 14 when the leadingcoefficient is less than Tc.

The estimation unit 14 outputs the estimated noise level (Step S16). Amethod of outputting the noise level is not particularly limited. Forexample, the estimation unit 14 may store the noise level into aspecified database, may transmit it to another computer, or may displayit on a monitor. The estimation unit 14 may output a set of the noiselevel and the input image.

In the case where the image processing device 10 processes a pluralityof input images, the processing of Steps S11 to S16 is repeated.

A method for using the estimated noise level is not particularlylimited. For example, the noise level may be used for noise removal inthe input image. By removing noise from the input image based on theestimated noise level, it is possible to improve the image quality ofthe input image. Further, by performing additional image processing suchas super-resolution on the image from which noise is removed, it ispossible to obtain a desired image without disturbance by noise. Theadditional image processing using the estimated noise level may beperformed by the image processing device 10 or may be performed byanother information processing device.

Program

An image processing program P1 that causes a computer to function as theimage processing device 10 is described hereinafter with reference toFIG. 8 . FIG. 8 is a view showing the configuration of the imageprocessing program P1.

The image processing program P1 includes a main module P10, anacquisition module P11, a generation module P12, a calculation moduleP13, and an estimation module P14. The main module P10 is a part thatexercises control over the estimation of the noise level. Theacquisition unit 11, the generation unit 12, the calculation unit 13 andthe estimation unit 14 are implemented by executing the acquisitionmodule P11, the generation module P1, the calculation module P13, andthe estimation module P14, respectively.

The image processing program P may be provided in the form of beingrecorded in a static manner on a tangible recording medium such asCD-ROM, DVD-ROM or semiconductor memory, for example. Alternatively, theimage processing program P1 may be provided as a data signalsuperimposed onto a carrier wave through a communication network.

Advantageous Effects

As described above, an image processing device according to one aspectof the present invention includes an acquisition unit configured toacquire an input image, a generation unit configured to generate aplurality of comparison images by superimposing each of a plurality ofnoises with different densities from each other on a target region beingat least part of the input image, a calculation unit configured tocalculate, for each of the plurality of comparison images, a degradationlevel of the comparison image with respect to the input image, and anestimation unit configured to estimate a noise level of the input imagebased on a plurality of calculated degradation levels.

An image processing method according to one aspect of the presentinvention is an image processing method performed by an image processingdevice including a processor, the method including an acquisition stepof acquiring an input image, a generation step of generating a pluralityof comparison images by superimposing each of a plurality of noises withdifferent densities from each other on a target region being at leastpart of the input image, a calculation step of calculating, for each ofthe plurality of comparison images, a degradation level of thecomparison image with respect to the input image, and an estimation stepof estimating a noise level of the input image based on a plurality ofcalculated degradation levels.

An image processing program according to one aspect of the presentinvention causes a computer to execute an acquisition step of acquiringan input image, a generation step of generating a plurality ofcomparison images by superimposing each of a plurality of noises withdifferent densities from each other on a target region being at leastpart of the input image, a calculation step of calculating, for each ofthe plurality of comparison images, a degradation level of thecomparison image with respect to the input image, and an estimation stepof estimating a noise level of the input image based on a plurality ofcalculated degradation levels.

In the above-described aspects, a plurality of comparison images areobtained by superimposing a plurality of noises on an input image. Theinput image is not substantially degraded when noise with lower densitythan inherent noise of the input image is superimposed on the inputimage. On the other hand, the input image is degraded when noise withhigher density than inherent noise of the input image is superimposed onthe input image. With use of such characteristics related to noise, bygenerating a plurality of comparison images from an input image andestimating the noise level of the input image using those comparisonimages, it is possible to estimate the absolute (or intrinsic) noiselevel of the input image without prior information. When a target imageis the entire input image, it is possible to obtain the noise level ofthe entire image rather than the local noise level of the image.

In an image processing program according to another aspect, thedegradation level may be structural similarity. Because the SSIM is anindex based on variations related to each of luminance, contrast andstructure, it is suitable for estimation of the noise level using thecomparison image obtained by superimposing noise on the input image.Thus, use of the SSIM enables accurate estimation of the noise level ofthe input image.

In an image processing program according to another aspect, theestimation unit may estimate the noise level by comparing the pluralityof degradation levels with a threshold. By using the threshold, it ispossible to estimate the noise level in simple processing and at highspeed.

In an image processing program according to another aspect, theestimation unit may estimate the noise level based on a leadingcoefficient of a non-linear function representing a relationship betweenthe density and the degradation level. The curvature of a graphindicating this non-linear function tends to vary according to the noiselevel regardless of the subject of the image, and the curvature issignificantly dependent on the leading coefficient of the non-linearfunction. It is thereby possible to accurately determine the noise levelbased on the leading coefficient.

In an image processing program according to another aspect, acorrespondence between the leading coefficient and the noise level maybe set in advance, and the estimation unit may calculate the leadingcoefficient of the non-linear function from the plurality of densitiesand the plurality of degradation levels, and determine the noise levelcorresponding to the calculated leading coefficient by referring to thecorrespondence. By using the correspondence prepared in advance, it ispossible to easily obtain the noise level from the leading coefficient.

Modified Example

An embodiment of the present invention is described in detail above.However, the present invention is not limited to the above-describedembodiment. Various changes and modifications may be made to the presentinvention without departing from the scope of the invention.

Although the image processing device 10 uses the SSIM as the degradationlevel in the above-described embodiment, another index may be used asthe degradation level.

Although the image processing device 10 uses the Gaussian noise in theabove-described embodiment, the type of noise to be superimposed on theinput image is not limited thereto. For example, the image processingdevice may superimpose another type of random noise on the input image.

Although the noise density (the standard deviation of the Gaussiannoise) is set as the noise level in the above-described embodiment, amethod of setting the noise level is not limited thereto. For example,the estimation unit may obtain the noise level from the noise density bypredetermined operation.

The procedure of the image processing method that is performed by atleast one processor is not limited to the example shown in the aboveembodiment. For example, some of the above-described steps (processing)may be skipped, or the steps may be performed in a different order.Further, any two or more steps of the above-described steps may becombined, or some of the steps may be modified or eliminated.Alternatively, another step may be performed in addition to theabove-described steps.

REFERENCE SIGNS LIST

10 . . . image processing device, 1 . . . acquisition unit, 12 . . .generation unit, 13 . . . calculation unit, 14 . . . estimation unit, P1. . . image processing program, P10 . . . main module, P11 . . .acquisition module, P12 . . . generation module, P13 . . . calculationmodule, P 14 . . . estimation module

The invention claimed is:
 1. An image processing device comprising: atleast one memory operable to store program code; and at least oneprocessor operable to read the program code and operate as instructed bythe program code, the program code being configured to cause the atleast one processor to: acquire an input image; generate a plurality ofcomparison images by superimposing each of a plurality of noises with aplurality of densities from each other on a target region being at leastpart of the input image; and estimate a noise level of the input imagebased on a plurality of degradation levels obtained by calculating, foreach of the plurality of comparison images, a degradation level of acomparison image with respect to the input image, wherein the programcode is further configured to cause the at least one processor toestimate the noise level based on a leading coefficient of a non-linearfunction representing a relationship between a density of a noise andthe degradation level, wherein a correspondence between the leadingcoefficient and the noise level is set in advance, and wherein theprogram code is further configured to cause the at least one processorto calculate the leading coefficient of the non-linear function from theplurality of densities and the plurality of degradation levels, anddetermine the noise level corresponding to the calculated leadingcoefficient by referring to the correspondence.
 2. An image processingmethod performed by an image processing device including a processor,comprising: acquiring an input image; generating a plurality ofcomparison images by superimposing each of a plurality of noises with aplurality of densities from each other on a target region being at leastpart of the input image; and estimating a noise level of the input imagebased on a plurality of degradation levels obtained by calculating, foreach of the plurality of comparison images, a degradation level of acomparison image with respect to the input image, wherein the estimatingthe noise level includes estimating the noise level based on a leadingcoefficient of a non-linear function representing a relationship betweena density of a noise and the degradation level, wherein a correspondencebetween the leading coefficient and the noise level is set in advance,and wherein the estimating the noise level based on the leadingcoefficient includes calculating the leading coefficient of thenon-linear function from the plurality of densities and the plurality ofdegradation levels, and determining the noise level corresponding to thecalculated leading coefficient by referring to the correspondence.